Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset.

Accurate detection of respiratory system damage including COVID-19 is considered one of the crucial applications of deep learning (DL) models using CT images. However, the main shortcoming of the published works has been unreliable reported accuracy and the lack of repeatability with new datasets, mainly due to slice-wise splits of the data, creating dependency between training and test sets due to shared data across the sets. We introduce a new dataset of CT images (ISFCT Dataset) with labels indicating the subject-wise split to train and test our DL algorithms in an unbiased manner.

A primary hierarchically organized patient-derived model enables in depth interrogation of stemness driven by the coding and non-coding genome.

Many cancers are organized as cellular hierarchies sustained by cancer stem cells (CSC), whose eradication is crucial for achieving long-term remission. Difficulties to isolate and undertake in vitro and in vivo experimental studies of rare CSC under conditions that preserve their original properties currently constitute a bottleneck for identifying molecular mechanisms involving coding and non-coding genomic regions that govern stemness. We focussed on acute myeloid leukemia (AML) as a paradigm of the CSC model and developed a patient-derived system termed OCI-AML22 that recapitulates the cellular hierarchy driven by leukemia stem cells (LSC).

Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease.

The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases.

Biological and therapeutic implications of a unique subtype of NPM1 mutated AML.

In acute myeloid leukemia (AML), molecular heterogeneity across patients constitutes a major challenge for prognosis and therapy. AML with NPM1 mutation is a distinct genetic entity in the revised World Health Organization classification. However, differing patterns of co-mutation and response to therapy within this group necessitate further stratification.

Large organized chromatin lysine domains help distinguish primitive from differentiated cell populations.

The human genome is partitioned into a collection of genomic features, inclusive of genes, transposable elements, lamina interacting regions, early replicating control elements and cis-regulatory elements, such as promoters, enhancers, and anchors of chromatin interactions. Uneven distribution of these features within chromosomes gives rise to clusters, such as topologically associating domains (TADs), lamina-associated domains, clusters of cis-regulatory elements or large organized chromatin lysine (K) domains (LOCKs). Here we show that LOCKs from diverse histone modifications discriminate primitive from differentiated cell types.

Single-cell chromatin accessibility profiling of glioblastoma identifies an invasive cancer stem cell population associated with lower survival.

Chromatin accessibility discriminates stem from mature cell populations, enabling the identification of primitive stem-like cells in primary tumors, such as glioblastoma (GBM) where self-renewing cells driving cancer progression and recurrence are prime targets for therapeutic intervention. We show, using single-cell chromatin accessibility, that primary human GBMs harbor a heterogeneous self-renewing population whose diversity is captured in patient-derived glioblastoma stem cells (GSCs). In-depth characterization of chromatin accessibility in GSCs identifies three GSC states: Reactive, Constructive, and Invasive, each governed by uniquely essential transcription factors and present within GBMs in varying proportions.

Pathway-Based Drug Response Prediction Using Similarity Identification in Gene Expression.

Lapatinib and trastuzumab (Herceptin) are targeted therapies designed for patients with HER2+ breast tumors. Although these therapies improved survival rates of patients with this tumor type, not all the patients harboring HER2 amplification respond to these drugs. The NeoALTTO clinical trial was designed to test whether a higher response rate can be achieved by combining lapatinib and trastuzumab.

GLUT1 inhibition blocks growth of RB1-positive triple negative breast cancer.

Triple negative breast cancer (TNBC) is a deadly form of breast cancer due to the development of resistance to chemotherapy affecting over 30% of patients. New therapeutics and companion biomarkers are urgently needed. Recognizing the elevated expression of glucose transporter 1 (GLUT1, encoded by SLC2A1) and associated metabolic dependencies in TNBC, we investigated the vulnerability of TNBC cell lines and patient-derived samples to GLUT1 inhibition.

Epigenetic Switch-Induced Viral Mimicry Evasion in Chemotherapy-Resistant Breast Cancer.

Tumor progression upon treatment arises from preexisting resistant cancer cells and/or adaptation of persister cancer cells committing to an expansion phase. Here, we show that evasion from viral mimicry response allows the growth of taxane-resistant triple-negative breast cancer (TNBC). This is enabled by an epigenetic state adapted to taxane-induced metabolic stress, where DNA hypomethylation over loci enriched in transposable elements (TE) is compensated by large chromatin domains of H3K27me3 to warrant TE repression.

SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology.

Drug-combination data portals have recently been introduced to mine huge amounts of pharmacological data with the aim of improving current chemotherapy strategies. However, these portals have only been investigated for isolated datasets, and molecular profiles of cancer cell lines are lacking. Here we developed a cloud-based pharmacogenomics portal called SYNERGxDB (http://SYNERGxDB.

Noncoding mutations target cis-regulatory elements of the FOXA1 plexus in prostate cancer.

Prostate cancer is the second most commonly diagnosed malignancy among men worldwide. Recurrently mutated in primary and metastatic prostate tumors, FOXA1 encodes a pioneer transcription factor involved in disease onset and progression through both androgen receptor-dependent and androgen receptor-independent mechanisms. Despite its oncogenic properties however, the regulation of FOXA1 expression remains unknown.

Identifying clusters of -regulatory elements underpinning TAD structures and lineage-specific regulatory networks.

Cellular identity relies on cell-type-specific gene expression controlled at the transcriptional level by -regulatory elements (CREs). CREs are unevenly distributed across the genome, giving rise to individual CREs and clusters of CREs (COREs). Technical and biological features hinder CORE identification.

SIGN: similarity identification in gene expression.

Motivation: High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes.

Results: We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples.

Tissue specificity of in vitro drug sensitivity.

Objectives: We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications.

Materials And Methods: We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values' area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity.

Results: We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.

Predictive approaches for drug combination discovery in cancer.

Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. This strategy aims at targeting multiple components of this complex disease. Despite the increasing number of drug combinations in use, many of them were empirically found in the clinic, and the molecular mechanisms underlying these drug combinations are often unclear.

Mathematical modelling of plasticity and phenotype switching in cancer cell populations.

The cancer stem cell (CSC) hypothesis suggests that cancer stem cells proliferate via a hierarchical model of unidirectional differentiation. However, growing experimental evidence has advanced this hypothesis by introducing a bidirectional hierarchy, in which non-CSCs may dedifferentiate into CSCs. Various models have been developed enabling the incorporation of this plasticity within cancer cell populations, focusing on behaviour in the limit of a large number of cells.

Mathematical modelling of phenotypic plasticity and conversion to a stem-cell state under hypoxia.

Hypoxia, or oxygen deficiency, is known to be associated with breast tumour progression, resistance to conventional therapies and poor clinical prognosis. The epithelial-mesenchymal transition (EMT) is a process that confers invasive and migratory capabilities as well as stem cell properties to carcinoma cells thus promoting metastatic progression. In this work, we examined the impact of hypoxia on EMT-associated cancer stem cell (CSC) properties, by culturing transformed human mammary epithelial cells under normoxic and hypoxic conditions, and applying in silico mathematical modelling to simulate the impact of hypoxia on the acquisition of CSC attributes and the transitions between differentiated and stem-like states.